The complexity of searching a graph
Journal of the ACM (JACM)
Randomized Pursuit-Evasion in Graphs
Combinatorics, Probability and Computing
Robot and Sensor Networks for First Responders
IEEE Pervasive Computing
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Visibility-based Pursuit-evasion with Limited Field of View
International Journal of Robotics Research
Multi-objective exploration and search for autonomous rescue robots: Research Articles
Journal of Field Robotics
A market-based framework for tightly-coupled planned coordination in multirobot teams
A market-based framework for tightly-coupled planned coordination in multirobot teams
Probabilistic planning for robotic exploration
Probabilistic planning for robotic exploration
Execution-time communication decisions for coordination of multi-agent teams
Execution-time communication decisions for coordination of multi-agent teams
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Randomized pursuit-evasion in a polygonal environment
IEEE Transactions on Robotics
GSST: anytime guaranteed search
Autonomous Robots
Improving the Efficiency of Clearing with Multi-agent Teams
International Journal of Robotics Research
Static and expanding grid coverage with ant robots: Complexity results
Theoretical Computer Science
Multi-agent Cooperative Cleaning of Expanding Domains
International Journal of Robotics Research
Connected searching of weighted trees
Theoretical Computer Science
Search and pursuit-evasion in mobile robotics
Autonomous Robots
Tracking an omnidirectional evader with a differential drive robot
Autonomous Robots
Target tracking without line of sight using range from radio
Autonomous Robots
Designing the HRTeam framework: lessons learned from a rough-and-ready human/multi-robot team
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Active planning for underwater inspection and the benefit of adaptivity
International Journal of Robotics Research
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This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the relationship between MESPP and the intuitive average capture time metric. Results show that our proposed linearly scalable approximation algorithm generates searcher paths that are competitive with those generated by exponential algorithms.